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Published on: November 10, 2023
Characteristics of hospitalised COVID-19 patients during the first two pandemic waves, Gauteng
Mpho L Sikhosana1, Waasila Jassat2, Zinhle Makatini1
1Department of Virology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Background:
Gauteng province (GP) was one of the most affected provinces in the country during the first two pandemic waves in South Africa. We aimed to describe the characteristics of coronavirus disease 2019 (COVID-19) patients admitted in one of the largest quaternary hospitals in GP during the first two waves.
Objectives:
Study objectives were to determine factors associated with hospital admission during the second wave and to describe factors associated with in-hospital COVID-19 mortality.
Method:
Data from a national hospital-based surveillance system of COVID-19 hospitalisations were used. Multivariable logistic regression models were conducted to compare patients hospitalised during wave 1 and wave 2, and to determine factors associated with in-hospital mortality.
Results:
The case fatality ratio was the highest (39.95%) during wave 2. Factors associated with hospitalisation included age groups 40-59 years (adjusted odds ratio [aOR]: 2.14, 95% confidence interval [CI]: 1.08-4.27), 60-79 years (aOR: 2.49, 95% CI: 1.23-5.02) and ≥ 80 years (aOR: 3.39, 95% CI: 1.35-8.49). Factors associated with in-hospital mortality included age groups 60-79 years (aOR: 2.55, 95% CI: 1.11-5.84) and ≥ 80 years (aOR: 5.66, 95% CI: 2.12-15.08); male sex (aOR: 1.56, 95% CI: 1.22-1.99); presence of an underlying comorbidity (aOR: 1.76, 95% CI: 1.37-2.26), as well as being admitted during post-wave 2 (aOR: 2.42, 95% CI: 1.33-4.42).
Conclusion:
Compared to the recent omicron-driven pandemic waves characterised by lower admission rates and less disease severity among younger patients, COVID-19 in-hospital mortality during the earlier waves was associated with older age, being male and having an underlying comorbidity.
Contribution:
This study showed how an active surveillance system can contribute towards identifying changes in disease trends.
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